Interference-weighted communication signal processing systems and methods

ABSTRACT

A level of interference affecting signal components of received communication signals is estimated and used to weight the signal components. The signal components in a each of a number of groups of signal components are weighted based on respective interference estimates to thereby adjust signal components for colored interference, which may vary significantly between different groups of signal components. Each group of signal components may include a single component or components within a relatively narrow sub-band of the communication signals, such as a coherence bandwidth of an Orthogonal Frequency Division Multiplexing (OFDM) signal.

FIELD OF THE INVENTION

This invention relates generally to communication systems and, inparticular, to processing of received communication signals.

BACKGROUND

Many communication signal processing systems and techniques are based onassumptions regarding noise and interference. In Orthogonal FrequencyDivision Multiplexing (OFDM) systems, for example, received signalprocessing typically assumes Additive White Gaussian Noise (AWGN) thatis constant across all sub-carriers or tones. In the presence ofdispersive channels for both a desired communication signal and one ormore interference signals, however, interference tends not to beconstant across channels. This phenomenon is often referred to as“coloured” interference. Interference caused by a single interferencesource can be coloured in the frequency domain, due to multi-patheffects, for example.

Conventional signal processing operations such as decoding anddemodulation of received signals are affected when these basicinterference assumptions do not hold. In the above example of OFDMsystems, information is often modulated onto sub-carriers at atransmitter using Quadrature Amplitude Modulation (QAM) techniques.Conventional QAM demapping or demodulation of information from receivedsignals in the presence coloured interference leads to increased blockor bit error rates (BLER/BER). Resultant performance losses can be onthe order of about a 7-9 dB carrier to interference (CIR) penalty for aparticular BLER or BER error floor.

One example type of communication system that is particularly prone tocoloured interference is multiple-cell and multiple-access OFDMcommunication systems, generally referred to as OFDMA systems, in whichfrequency hopping is used by neighbouring cells and the hopping patternsare not orthogonal. In such systems, it is possible that mobile stationsin adjacent cells may hop to the same sub-carrier at the same time,resulting in a relatively high level of interference on that sub-carrierbut not necessarily on other sub-carriers. If sufficient informationabout interference sources, mainly channel state information, isavailable, then interference can be cancelled. However, the cost ofinterference cancellation in terms of signal processing can besignificant. In addition, for up-link transmissions from mobile stationsto base stations, it is unlikely that each base station can know thebehaviour of all the mobile stations in its neighbouring cells.Therefore, cancellation of such inter-cell interference is not apractical option.

One way to reduce inter-cell interference is by scheduling mobilestations in coordinated patterns for neighbouring cells. Combined withup-link power control, coordination of mobile stations can mitigateinter-cell interference to a certain degree, but introduces additionalcommunication system requirements to provide for such control of mobilestation operations across different cells.

SUMMARY OF THE INVENTION

According to one aspect of the invention, a communication signalreceiver includes an interference estimator and a communication signalprocessing module. The interference estimator estimates interference inreceived communication signals. The communication signal processingmodule receives communication signals that include multiple signalcomponents and weights the signal components in a each of a number ofgroups of signal components based on respective interference estimatesfrom the interference estimator to thereby adjust signal components forcoloured interference.

Each of the groups of signal components includes at least one signalcomponent. In a preferred embodiment, the communication signals are OFDMsignals and the signal components are sub-carrier signals. In a furtherpreferred embodiment, the groups of signal components includesub-carrier signals within a coherence bandwidth of the OFDM signals.

The communication signal processing module may include a decoderconnected to the interference estimator and an antenna system. Thedecoder is preferably an Space-Time Transmit Diversity (STTD) decoder, aMultiple Input Multiple Output (MIMO) decoder, or some other type ofdiversity decoder.

In accordance with a preferred embodiment of the invention, the decodercombines weighted signal components of different received communicationsignals to generate decoded communication signals. In anotherembodiment, the communication processing module also includes ademodulator connected to the decoder for demodulating decodedcommunication signal components output from the decoder and a multiplierconnected to the demodulator and to the interference estimator forweighting groups of demodulated communication signal components outputfrom the demodulator based on respective interference estimates from theinterference estimator. The demodulator is preferably a QAM demodulatoror a Quadrature Phase Shift Keying (QPSK) demodulator.

The invention also provides, in another aspect, a method of processingcommunication signals. Communication signals having multiple signalcomponents are received, and interference affecting the receivedcommunication signals is estimated. Groups of the signal components arethen weighted using respective interference estimates. The method may beembodied, for example, in a computer program product having acomputer-readable medium storing instructions for performing the method.

Other aspects and features of the present invention will become apparentto those ordinarily skilled in the art upon review of the followingdescription of the specific embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described in greater detail with reference tothe accompanying diagrams, in which:

FIG. 1 is block diagram of a communication signal receiver systemincorporating an embodiment of the invention;

FIG. 2 is a block diagram of an example of a communication signalprocessing module and an interference estimator according to anembodiment of the invention;

FIG. 3 is a block diagram of an example of a communication signalprocessing module and an interference estimator according to anotherembodiment of the invention;

FIG. 4 is a flow diagram of a method in accordance with an embodiment ofthe invention; and

FIG. 5 is a plot illustrating an example signal to noise ratio (SNR)gain characteristic relative to interference imbalance.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 is block diagram of a communication signal receiver systemincorporating an embodiment of the invention. An antenna system 10 isconnected to a communication signal processing module 14 in the receiver12. The communication signal processing module 14 is also connected toan interference estimator 16 and to other components of the receiver 12.Although shown as separate blocks in FIG. 1, the communication signalprocessing module 14 and the interference estimator 16 may beimplemented in a single processor, as software or in a digital signalprocessor (DSP), for example.

It should be appreciated that the communication signal receiver systemas shown in FIG. 1 is solely for illustrative purposes. Actualimplementations of the present invention may include further, fewer, ordifferent components than those shown in FIG. 1. For instance, manycommunication devices use the same antenna for receiving andtransmitting communication signals, such that the antenna 10 isconnected to both the receiver 12 and a transmitter (not shown) or to atransceiver incorporating the receiver 12. The nature and operation ofreceiver components will also be different for receivers configured toreceive different types of communication signal, such as receiversdesigned to operate within different types of communication networks.

Although a single antenna system 10 is shown in FIG. 1, more than onereceiving antenna may be provided therein. In one embodiment, a singleantenna receives communication signals from one or more transmittingantennas (not shown). According to other embodiments, multiple antennasare connected to the receiver 12 and configured to receive communicationsignals from one or more transmitting antennas. For example, in a 2-by-1Space-Time Transmit Diversity (STTD) system, the antenna system 10includes a single antenna for receiving communication signals from twotransmitting antennas. In a 2-by-2 STTD system, two antennas areprovided and configured to receive communication signals from twotransmitting antennas. Similarly, in 1-by-2 receive diversity systems,two antennas are adapted to receive communication signals from onetransmitting antenna. Multiple Input Multiple Output (MIMO) systems alsoemploy multiple receiving and transmitting antennas. Other receiving andtransmitting antenna arrangements in conjunction with which embodimentsof the present invention may be implemented will be apparent to thoseskilled in the art.

The communication signal processing module 14 performs such processingoperations as decoding and/or demodulation of received signals intoformats suitable for other receiver components. As will be apparent tothose skilled in the art, the receiver 12 will typically be connected toother components in a communication device and performs any operationsnecessary to provide communication signals or the information containedtherein to the other components. Information in a communication signalmight be destined for a software application being executed by aprocessor in a mobile station, for example.

In a preferred embodiment, the communication signal processing module 14is implemented at least partially in software, stored in acomputer-readable memory, that is executable by a processor. One suchcontemplated implementation of the communication signal processingmodule 14 is a digital signal processor (DSP), which may also performother operations in addition to those described herein. According to analternative embodiment, a processor of a communication device in whichthe receiver 12 is implemented is configured to execute communicationsignal processing software.

As described above, communication signals are often affected byinterference. Although the antenna system 10 is intended to receivedesired communication signals, interference signals are also received.The interference estimator 16, in accordance with an embodiment of theinvention, estimates interference in received communication signals andprovides interference estimates, or interference weights based oninterference estimates, to the communication signal processing module14. The interference estimates or weights are then used by thecommunication signal processing module 14 in subsequent processing of areceived communication signal, as described in further detail below.Where weights are calculated by the interference estimator 16, theinterference estimator 16 comprises, or can be considered to be, aweight generator that determines interference weights.

Returning to the above illustrative example of OFDM and OFDMA systems, acommunication signal is spread over multiple sub-carriers and thuscomprises a plurality of signal components. The common assumption thatinterference is constant over all sub-carriers is not always valid,especially in systems such as IEEE 802.16 and Universal MobileTelecommunications System (UMTS) where fast sub-carrier hopping isapplied. It will be apparent to those skilled in the art that “IEEE802.16” refers to a set of specifications, available from the Instituteof Electrical and Electronics Engineers (IEEE), relating to wirelessMetropolitan Area Networks (MANs). In an embodiment of the invention forOFDM or OFDMA, the interference estimator 16 estimates interference ateach sub-carrier in the communication signal, and the received signalprocessing module 16 processes the communication signal using theinterference estimates or weights calculated using such estimates. Forexample, in one embodiment of the invention suitable for QAM-based OFDMor OFDMA, interference weights for each sub-carrier are generated by theinterference estimator 16 and used in soft QAM demodulation, alsoreferred to as soft demapping.

FIG. 2 is a block diagram of an example of a communication signalprocessing module and an interference estimator according to anembodiment of the invention. As shown, an antenna system 20 is connectedto a receiver 22, which includes an interference estimator 26 and acommunication signal processing module 24. The communication signalprocessing module 24 includes a decoder 27 connected at its output toboth a demodulator 28 and the interference estimator 26 and a multiplier29 connected to the outputs of the interference estimator 26 and thedemodulator 28.

The particular embodiment shown is illustrative of one type ofimplementation of the invention, in which interference estimates areused to weight demodulated communication signals. As will becomeapparent from the following description, the invention is in no waylimited to such an implementation. Interference estimates may be used inother signal processing operations, and in receivers having componentsother than those specifically shown in FIG. 2. For example, not alltypes of receiver include both a decoder 27 and a demodulator 28. Inaddition, conversions between time and frequency domain signals aretypically facilitated by such transforms as the Fast Fourier Transform(FFT) and Inverse FFT (IFFT). Those skilled in the art will be familiarwith such signal operations and their application to communicationsignal processing.

The antenna system 20 and the interference estimator 26 aresubstantially similar to the antenna system 10 and the interferenceestimator 16 described above. The antenna system 20 represents one ormore antennas for receiving communication signals from one or moretransmitters (not shown), and the interference estimator 26 estimatesthe interference in received communication signals.

The decoder 27 is, for instance, a space-time decoder in an STTD systemthat decodes received communication signals from one or morecommunication channels into modulation symbols. In OFDM and OFDMAsystems, the decoder 27 decodes symbols from a plurality of sub-carriersover which communication signals are spread. As will be apparent tothose skilled in the art, a decoding algorithm implemented in thedecoder 27 is the inverse of an encoding algorithm used at a transmitterfrom which a received communication signal is received.

The demodulator 28 processes modulation symbols in the decoded signalsto extract information that was modulated onto a signal by atransmitter, and is sometimes referred to as a demapper. In OFDM andOFDMA systems for instance, information is modulated onto a plurality ofsub-carriers. QAM is one modulation scheme that is commonly used in OFDMand OFDMA. In a QAM-based OFDM or OFDMA system, the demodulator 28 is aQAM demodulator.

In the-system of FIG. 2, the multiplier 29 applies interferenceestimates or interference weights from the interference estimator 26 todemodulated signals output from the demodulator 28. The demodulatedsignals are thereby adjusted for interference, which is estimated basedon received communication signals. The received signal processing module24 is thus less dependent upon conventional assumptions of uniforminterference.

The operation of the receiver of FIG. 2 will now be described in thecontext of a specific illustrative example of a 2-by-1 STTD QAM-basedOFDM or OFDMA system, in which the antenna system 20 is configured toreceive communication signals from two transmitting antennas (notshown), the decoder 27 is an STTD decoder, and the demodulator 28 is asoft QAM demapper. Generalization to other configurations such as 2-by-2STTD, as well as non-STTD and/or non-QAM communication schemes will bestraightforward to those skilled in the art. The interference weightingtechniques described and claimed herein are not limited to anyparticular communication scheme. The detailed examples below arepresented solely for illustrative purposes and are not intended to limitthe scope of the invention.

At the receiver 22, un-normalized STTD decoder outputs from the decoder27 for a 2-by-1 system can be expressed as in equation (1){tilde over (s)} ₁(k)=(|h ₁(k)|² +|h ₂(k)|²)s ₁(k)+h ₁*(k)n ₁(k)+h₂(k)n₂*(k){tilde over (s)} ₂(k)=(|h ₁(k)|² +|h ₂(k)|²)s ₂(k)−h ₁(k)n ₂*(k)+h²*(k)n ₁(k)′  (1)where

k is an index of OFDM/OFDMA sub-carriers;

{tilde over (s)}₁(k) and {tilde over (s)}₂(k) are the decoder outputscorresponding to communication signals received from the twotransmitting antennas;

h₁(k) and h₂(k) are elements of the 2-by-1 STTD, channel matrix; and

n₁(k) and n₂(k) represent interference affecting the communicationsignals received from the two transmitting antennas.

Where the variance of n_(i)(k) is σ_(i) ²(k), h₁*(k)n₁(k) andh₂(k)n₂*(k) are independent of each other, andE[h₁*(k)n_(,1)(k)]=E[h₂(k)n₂*(k)]=0, then the noise power of {tilde over(s)}₁(k) isσ²(k)=(|h ₁(k)|²σ₁ ²(k)+|h ₂(k)|²σ₂ ²(k)).  (2)

Designating {right arrow over (b)}(k)=[b₁(k) b₂(k) . . . b_(q)(k)] asthe q bits of a QAM symbol mapped to an actual transmitted signal s₁(k)at a QAM modulator at a transmitter, a soft demapping formula for {rightarrow over (b)}(k) can be derived. As those skilled in the art willappreciate, the process for s₂(k) demapping is obtained in a similarway. For the sake of simplicity, the sub-carrier index k is dropped fromthe following derivation. However, the results are applicable to eachspecific sub-carrier. Interference weighting is thus applicable on a persub-carrier basis. Each sub-carrier signal component of a receivedcommunication signal is thereby weighted according to estimatedinterference.

It is known that the log likelihood ratio (LLR) for the lth element of{right arrow over (b)}, b₁, is given by

$\begin{matrix}{{{\Lambda\left( b_{l} \right)} = {\log\frac{\Pr\left\lbrack {b_{l} = \left. 1 \middle| \overset{\rightharpoonup}{r} \right.} \right\rbrack}{\Pr\left\lbrack {b_{l} = \left. 0 \middle| \overset{\rightharpoonup}{r} \right.} \right\rbrack}}},} & (3)\end{matrix}$where {right arrow over (r)} represents a received communication signal.By substituting for the above interference statistics, equation (3) canbe further expressed as

$\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\log{\frac{\sum\limits_{{s_{1} = {f{(\overset{\rightharpoonup}{b})}}},{b_{l} = 1}}{\exp\left( {- \frac{{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2}}{2\left( {{{h_{1}}^{2}\sigma_{1}^{2}} + {{h_{2}}^{2}\sigma_{2}^{2}}} \right)}} \right)}}{\sum\limits_{{s_{1} = {f{(\overset{\rightharpoonup}{b})}}},{b_{l} = 0}}{\exp\left( {- \frac{{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2}}{2\left( {{{h_{1}}^{2}\sigma_{1}^{2}} + {{h_{2}}^{2}\sigma_{2}^{2}}} \right)}} \right)}}.}}} & (4)\end{matrix}$

Since it is impractical to calculate equation (4), max-log is normallyused instead. This simplifies equation (4) to

$\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\frac{\begin{matrix}{{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 0}}{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2}} -} \\{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 1}}{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2}}\end{matrix}}{{{h_{1}}^{2}\sigma_{1}^{2}} + {{h_{2}}^{2}\sigma_{2}^{2}}}.}} & (5)\end{matrix}$

A common assumption in conventional received signal processingtechniques is that interference is constant (i.e., σ₁ ²=σ₂ ²) over anerror-correcting code block used at a transmitter for encoding thecommunication signal. Equation (5) is then further simplified to

$\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 0}}{\frac{\begin{matrix}{{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2} -} \\{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 1}}{{{\overset{\sim}{s}}_{1} - {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)s_{1}}}}^{2}}\end{matrix}}{{h_{1}}^{2} + {h_{2}}^{2}}.}}} & (6)\end{matrix}$

However, as described above, σ₁ ² and σ₂ ² vary from sub-carrier tosub-carrier for coloured interference systems.

In order to simplify implementation without making assumptions aboutinterference characteristics, QAM demapping is performed after QAMnormalization in accordance with an aspect of the invention. Thus,equation (5) can be expressed as

$\begin{matrix}\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\frac{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)^{2}}{{{h_{1}}^{2}\sigma_{1}^{2}} + {{h_{2}}^{2}\sigma_{2}^{2}}}\left( {{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 0}}{{\frac{{\overset{\sim}{s}}_{1}}{{h_{1}}^{2} + {h_{2}}^{2}} - s_{1}}}^{2}} -} \right.}} \\{\left. {\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 1}}{{\frac{{\overset{\sim}{s}}_{1}}{{h_{1}}^{2} + {h_{2}}^{2}} - s_{1}}}^{2}} \right).}\end{matrix} & (7)\end{matrix}$

As above, a conventional assumption of σ₁ ²=σ₂ ² being constant over acode block allows equation (7) to be simplified to

$\begin{matrix}\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)\left( {{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 0}}{{\frac{{\overset{\sim}{s}}_{1}}{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)} - s_{1}}}^{2}} -} \right.}} \\{\left. {\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 1}}{{\frac{{\overset{\sim}{s}}_{1}}{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)} - s_{1}}}^{2}} \right).}\end{matrix} & (8)\end{matrix}$

For a coloured interference system, however, interference is notconstant over a code block, and QAM soft-demapping is preferably dividedinto two steps:

-   1) calculate LLR based on a normalized STTD output, from equation    (8)

$\begin{matrix}\begin{matrix}{{\overset{\sim}{\Lambda}\left( b_{l} \right)} = {{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 0}}{{\frac{{\overset{\sim}{s}}_{1}}{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)} - s_{1}}}^{2}} -}} \\{{\min\limits_{{s_{1} = {f{(b)}}},{b_{l} = 1}}{{\frac{{\overset{\sim}{s}}_{1}}{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)} - s_{1}}}^{2}};}\end{matrix} & (9)\end{matrix}$and

-   2) weight {tilde over (Λ)}(b₁) with estimated interference power    Λ(b₁)=α{tilde over (Λ)}(b₁),  (10)    when

$\begin{matrix}{\alpha = {\frac{\left( {{h_{1}}^{2} + {h_{2}}^{2}} \right)^{2}}{{{h_{1}}^{2}\sigma_{1}^{2}} + {{h_{2}}^{2}\sigma_{2}^{2}}}.}} & (11)\end{matrix}$

Where σ₁ ²=σ₂ ², i.e., the noise/interference power does not changewithin one STTD code block, then equation (11) can be further simplifiedto

$\begin{matrix}{\alpha = {\frac{{h_{1}}^{2} + {h_{2}}^{2}}{\sigma_{1}^{2}}.}} & (12)\end{matrix}$

By comparing equation (12) with equation (8), we can see that the onlydifference between the two is the interference weighting factor

$\frac{1}{\sigma_{1}^{2}}.$

Now the question is how to estimate the coloured interferences σ₁ ² andσ₂ ². Note that conventional derivations assume that σ₁ ² and σ₂ ² areGaussian distributed.

In order to implement equation (11), the term (|h₁|²σ₁ ²+h₂|²σ₂ ²) is tobe estimated. In addition, this estimation is preferably based on acurrent received communication signal. If the estimation is based onprevious signals or data, the interference estimate may not be accurate,as interference may not be the same for a current signal, due tosub-carrier hopping, for example.

To estimate α, it is observed from equation (1) that

$\begin{matrix}{\frac{{{h_{1}^{*}(k)}{n_{1}(k)}} + {{h_{2}(k)}{n_{2}^{*}(k)}}}{\left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right.} = {\frac{{\overset{\sim}{s}}_{1}(k)}{\left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right.} - {{s_{1}(k)}.}}} & (13)\end{matrix}$

If h₁*(k)n₁(k) and h₂(k)n₂*(k) are independent of each other andE[h₁*(k)n_(,1)(k)]=E[h₂(k)n₂*(k)]=0, then we have

$\begin{matrix}\begin{matrix}{\frac{1}{\alpha} = {E\left\lbrack \left| \frac{{{h_{1}^{*}(k)}{n_{1}(k)}} + {{h_{2}(k)}{n_{2}^{*}(k)}}}{\left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right.} \right|^{2} \right\rbrack}} \\{= \frac{\left| {h_{1}(k)} \middle| {}_{2}{{\sigma_{1}^{2}(k)} +} \middle| {h_{2}(k)} \middle| {}_{2}{\sigma_{2}^{2}(k)} \right.}{\left( \left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|} \right. \right)^{2}}} \\{= {{E\left\lbrack \left| {\frac{{\overset{\sim}{s}}_{1}(k)}{\left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right.} - {s_{1}(k)}} \right|^{2} \right\rbrack}.}}\end{matrix} & (14)\end{matrix}$Therefore,

$\begin{matrix}{\alpha = {\frac{1}{E\left\lbrack \left| {\frac{{\overset{\sim}{s}}_{1}(k)}{\left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right.} - {s_{1}(k)}} \right|^{2} \right\rbrack}.}} & (15)\end{matrix}$

Since the exact transmitted symbol s₁(k) is difficult to ascertain atthe time of interference estimation, we use a hard-decision value ŝ₁(k)from the decoder 27 instead. In addition, assuming that interference isconstant within the coherence bandwidth constituted of N_(c)sub-carriers, then equation (14) can be rewritten as

$\begin{matrix}\begin{matrix}{\frac{1}{\alpha} = \frac{\left| {h_{1}(k)} \middle| {}_{2}{{\sigma_{1}^{2}(k)} +} \middle| {h_{2}(k)} \middle| {}_{2}{\sigma_{2}^{2}(k)} \right.}{\left( \left| {h_{1}(k)} \middle| {}_{2}{+ \left| {h_{2}(k)} \right|^{2}} \right. \right)^{2}}} \\{\left. {\approx {\frac{1}{N_{c}}\sum\limits_{i = 1}^{N_{c}}}}\; \middle| {\frac{{\overset{\sim}{s}}_{1}\left( {{vN}_{c} + i} \right)}{\left| {h_{1}\left( {{vN}_{c} + i} \right)} \middle| {}_{2}{+ \left| {h_{2}\left( {{vN}_{c} + i} \right)} \right|^{2}} \right.} - {{\hat{s}}_{1}\left( {{vN}_{c} + i} \right)}} \right|^{2}}\end{matrix} & (16)\end{matrix}$where ν is an integer. By observing the right side of equation (16), itcan be seen that this is in fact the average noise/interference power inthe normalized STTD signal domain. In actual implementations, theconstant factor

$\frac{1}{N_{c}}$can be omitted.

Therefore, it will be apparent that interference weighting may involveweighting each signal component in a received communication signal basedon a respective per-signal component interference estimate or weightingeach signal component in each of a number of groups of signal componentsbased on a respective per-group interference estimate. Sub-carriersignal components within a coherence bandwidth represent one example ofa group of signal components that may be weighted using a per-groupinterference estimate.

In accordance with an embodiment of the invention, interference weightedQAM soft-demapping for multi-cell OFDM systems is achieved by applyingequation (16) to equation (11). Signal components associated with eachsub-carrier are preferably weighted for interference.

Note that since s₂(k) is experiencing the same level of interference ass₁(k), the estimated α for s₂(k) can be averaged with that from s₁(k) toprovide a better estimate for interference weighting, provided thatinterference power remains the same within one STTD block.

It should be appreciated that coloured interferences may change acrossthe frequency domain while remaining substantially constant within arelatively small sub-band such as a coherence bandwidth or betweenmultiple receiving antennas. However, interference levels can changesignificantly from sub-band to sub-band, and as such, conventionalassumptions that interference is constant across all sub-bands do nothold for coloured interferences.

Interference weighting for soft demapping may also be applied to othertypes of systems, such as MIMO.

Consider an M×N MIMO system, with a channel matrix defined as

$\begin{matrix}{H = {\begin{bmatrix}h_{11} & h_{12} & \ldots & h_{1M} \\h_{21} & h_{22} & \ldots & h_{2M} \\\vdots & \vdots & \ddots & \vdots \\h_{N1} & h_{N2} & \ldots & h_{NM}\end{bmatrix}.}} & (17)\end{matrix}$

In this case, a received signal may be expressed as{right arrow over (r)}=H{right arrow over (s)}+{right arrow over(n)},  (18)where

$\begin{matrix}{{\overset{\rightharpoonup}{r} = \left\lbrack {r_{1}\mspace{14mu} r_{2}\mspace{14mu}\ldots\mspace{14mu} r_{N}} \right\rbrack^{T}}{\overset{\rightharpoonup}{s} = {{\left\lbrack {s_{1}\mspace{14mu} s_{2}\mspace{14mu}\ldots\mspace{14mu} s_{M}} \right\rbrack^{T}.\overset{\rightharpoonup}{n}} = \left\lbrack {n_{1}\mspace{14mu} n_{2}\mspace{14mu}\ldots\mspace{14mu} n_{N}} \right\rbrack^{T}}}} & (19)\end{matrix}$

With QAM modulation applied to a signal at a transmitting side using aconstellation size of 2^(q), the received signal {right arrow over (r)}corresponds to qM bits. The task of soft-demapping, as above, is tocompute the LLR for these qM bits.

For MIMO, the decoder 27 may be a maximum likelihood (ML) decoder, forexample. Those skilled in the art will appreciate that an ML decoder isdefined by{right arrow over (ŝ)}=min∥{right arrow over (r)}−H{right arrow over(s)}∥,  (20)when the components in {right arrow over (n)} are drawn fromindependent, identically distributed wide-sense stationary processeswith variance σ².

Interference weighting at a demapping stage as shown in FIG. 2 isapplicable when the interference levels experienced by all receiveantennas in a MIMO receiver are the same. In this case, from equations(18) and (20), the power of the overall interference can be shown as

$\begin{matrix}{{{\overset{\rightharpoonup}{n}} = {{{\overset{\rightharpoonup}{r} - {H\overset{\rightharpoonup}{s}}}} = {\sum\limits_{i = 1}^{N}\;{{r_{i} - {H_{i}\overset{\rightharpoonup}{s}}}}^{2}}}},} & (21)\end{matrix}$where H_(i) denotes the i-th row of H. Those skilled in the art willappreciate that the soft-demapping process is then determined by

$\begin{matrix}\begin{matrix}{{\Lambda\left( b_{l} \right)} = {\frac{1}{\sigma^{2}}\left( {\min\limits_{\overset{\rightharpoonup}{s} = {{f{(\overset{\rightharpoonup}{b})}}{({,{b_{l} = 0}}}}}\sum\limits_{i = 1}^{N}}\; \middle| {r_{i} - {\sum\limits_{j = 1}^{M}\;{h_{ij}s_{j}}}} \middle| {}_{2} - \right.}} \\{\left. \left. {\min\limits_{{\overset{\rightharpoonup}{s} = {f{(\overset{\rightharpoonup}{b})}}},{b_{l} = 1}}\sum\limits_{i = 1}^{N}}\; \middle| {r_{i} - {\sum\limits_{j = 1}^{M}\;{h_{ij}s_{j}}}} \right|^{2} \right).}\end{matrix} & (22)\end{matrix}$

The variance σ² is estimated based on equation (21). A better estimateof σ² may be generated as an average over several sub-carriers in asub-band, such as within the coherence bandwidth. Assuming thatinterference is constant within the coherence bandwidth constituted ofN_(c) sub-carriers, then the noise/interference variance σ² can beestimated as

$\begin{matrix}{\left. {\sigma^{2} \approx {\frac{1}{N_{c}}{\sum\limits_{k = 1}^{N_{c}}\;\sum\limits_{i = 1}^{N}}}}\; \middle| {{r_{i}\left( {{vN}_{c} + k} \right)} - {{H_{i}\left( {{vN}_{c} + k} \right)}{\overset{\rightharpoonup}{s}\left( {{vN}_{c} + k} \right)}}} \right|^{2},} & (23)\end{matrix}$where ν is an integer, and the sub-carrier indexes (νN_(c)+1 νN_(c)+2 .. . (v+1)N_(c)) cover the sub-band that the estimated σ² is to beapplied. The constant factor

$\frac{1}{N_{c}}$in equation (23) can be omitted in implementation.

Referring now to FIG. 3, a block diagram of an example of acommunication signal processing module and an interference estimatoraccording to another embodiment of the invention is shown. In FIG. 3, areceiver 32 is connected to an antenna system 30, and includes aninterference estimator 36 and a communication signal processing module34. The communication signal processing module 34 comprises a decoder 37and a demodulator 38 connected to an output of the decoder 37. Both theinterference estimator 36 and the decoder 37 are connected to theantenna system 30, and the decoder 37 is further connected to an outputof the interference estimator 36. The interference estimator is alsoconnected to an output of the decoder 37.

As described above, the antenna system 30 is adapted to receivecommunication signals from one or more transmitting antennas (notshown), the interference estimator 36 estimates interference in receivedcommunication signals, the decoder 37 implements a decoding scheme thatsubstantially corresponds to an encoding scheme employed at atransmitting side, and the demodulator 38 demodulates informationmodulated onto one or more carrier signals at the transmitting side.

Comparing FIGS. 2 and 3, it will be apparent that the interferenceestimator 36 in the embodiment of FIG. 3 operates in conjunction withthe decoder 37 instead of the demodulator 38. Thus, in this embodimentof the invention, decoding is interference weighted. Demodulation maythen be performed in accordance with conventional techniques, asinterference has already been compensated to some degree in the decoder.

As above, the operation of the receiver 32 will be described in detailby way of illustrative examples of STTD and MIMO. Extension of theprinciples applied in these examples to other communication schemes willbe apparent to those skilled in the art.

One of the decoding operations performed by an STTD decoder is receivedsignal combining. In a 2-by-2 STTD system for example, the antennasystem 30 includes two antennas adapted to receive communication signalsfrom two transmitting antennas, and a channel matrix is defined as

$\begin{matrix}{H = {\begin{bmatrix}h_{11} & h_{12} \\h_{21} & h_{22}\end{bmatrix}.}} & (24)\end{matrix}$

Where the channel matrix H and noise/interference variances at the tworeceiving antennas σ₁ ² and σ₂ ² do not change within one STTD codingblock, the received signals from each antenna i at time j, denotedr_(i,j), can be expressed in terms of the transmitted signals s₁ and s₂asr _(1,1) =h ₁₁ s ₁ +h ₁₂ s ₂ +n _(1,1)r _(1,2) =−h ₁₁ s ₂ *+h ₁₂ s ₁ *+n _(1,2)r _(2,1) =h ₂₁ s ₁ +h ₂₂ s ₂ +n _(2,1)r _(2,2) =−h ₂₁ s ₂ *+h ₂₂ s ₁ *+n _(2,2).  (25)

It is known, for example from S. M. Alamouti, “A Simple Transmitter.Diversity Technique for Wireless Communications,” IEEE J. Select. AreasCommun., Vol. 16, pp. 1451-1458, October 1998, that the STTD combinedsignals areŝ ₁ =h ₁₁ *r _(1,1) +h ₁₂ r _(1,2) *+h ₂₁ *r _(2,1) +h ₂₂ r _(2,2)ŝ ₂ =h ₁₂ *r _(1,1) −h ₁₁ r _(1,2) *+h ₂₂ *r _(2,1) −h ₂₁ r_(2,2)*,  (26)which can be further expressed asŝ ₁=(|h ₁₁|² +|h ₁₂|² +|h ₂₁|² +|h ₂₂|²)s ₁ +h ₁₁ *n _(1,1) +h ₁₂ n_(1,2) *+h ₂₁ *n _(2,1) +h ₂₂ n _(2,2)*,ŝ ₂=(|h ₁₁|² +|h ₁₂|² +|h ₂₁|² +|h ₂₂|²)s ₂ −h ₁₁ n _(1,2) *+h ₁₂ *n_(1,1) −h ₂₁ n _(2,2) *+h ₂₂ *n _(2,1).  (27)

Where E[h₁₁*n_(1,1)]=E[h₁₂n_(1,2)*]=E[h₂₁*n_(2,1)]=E[h₂₂n_(2,2)*]=0, andthey are independent of each other, then the noise/interference power inŝ₁ isP _(noise)(ŝ ₁)=(|h ₁₁|² +|h ₁₂|²)σ₁ ²+(|h ₂₁|² +|h ₂₂|²)σ₂ ².  (28)

Similarly, it can be shown that P_(noise)(ŝ₁)=P_(noise)(ŝ₂). Thus, thesignal to noise ratio (SNR) of an STTD signal decoded in this manner isgiven by

$\begin{matrix}{{SNR}_{EW} = {\frac{\left( {{h_{11}}^{2} + {h_{12}}^{2} + {h_{21}}^{2} + {h_{22}}^{2}} \right)^{2}}{{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)\sigma_{1}^{2}} + {\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)\sigma_{2}^{2}}}{{s_{i}}^{2}.}}} & (29)\end{matrix}$

When σ₁ ²=σ₂ ², equation (29) simplifies to

$\begin{matrix}{{SNR}_{EW} = {\frac{{h_{11}}^{2} + {h_{12}}^{2} + {h_{21}}^{2} + {h_{22}}^{2}}{\sigma_{1}^{2}}{{s_{i}}^{2}.}}} & (30)\end{matrix}$

However, when σ₁ ²≠σ₂ ², equation (27) does not provide optimal signalcombining. In accordance with an embodiment of the invention, signalcombining is interference weighted.

Equation (27) may be rewritten asŝ ₁ =ŝ _(1,1) +ŝ _(1,2)ŝ ₂ =ŝ _(2,1) +ŝ _(2,2),  (31)whereŝ _(1,1)=(|h ₁₁|² +h ₁₂|²)s ₁ +h ₁₁ * _(n) _(1,1) +h ₁₂ n _(1,2)*ŝ _(1,2)=(|h ₂₁|² +h ₂₂|²)s ₁ +h ₂₁ *n _(2,1) +h ₂₂ n _(2,2)*ŝ _(2,1)=(|h ₁₁|² +h ₁₂|²)s ₂ +h ₁₁ _(n) _(1,2) *+h ₁₂ *n _(1,1)ŝ _(2,2)=(|h ₂₁|² +h ₂₂|²)s ₂ +h ₂₁ n _(2,2) *+h ₂₂ *n _(2,1).  (32)

It is not difficult to see that ŝ_(i,j) represents the components of thereceived signal for s_(i) from antenna j, and equation (31) representsan equal combining of the signals received from the two transmittingantennas at each receiving antenna. It is this equal combining that canpotentially degrade system performance in the presence of colouredinterference.

With interference-weighted combining, equation (31) is modified to

$\begin{matrix}{{\hat{s}}_{1} = {\frac{{\hat{s}}_{1,1}}{\sigma_{1}^{2}} + \frac{{\hat{s}}_{1,2}}{\sigma_{2}^{2}}}} & (33) \\{{\hat{s}}_{2} = {\frac{{\hat{s}}_{2,1}}{\sigma_{1}^{2}} + {\frac{{\hat{s}}_{2,2}}{\sigma_{2}^{2}}.}}} & \;\end{matrix}$obviously, when σ₁ ²=σ₂ ², equation (33) simplifies to equation (31).

Applying equation (33) to equation (27), to provideinterference-weighted signal combining,

$\begin{matrix}\begin{matrix}{{\hat{s}}_{1} = {{\left( {\frac{{h_{11}}^{2} + {h_{12}}^{2}}{\sigma_{1}^{2}} + \frac{{h_{21}}^{2} + {h_{22}}^{2}}{\sigma_{2}^{2}}} \right)s_{1}} +}} \\{\mspace{50mu}{\frac{{h_{11}^{*}n_{1,1}} + {h_{12}n_{1,2}^{*}}}{\sigma_{1}^{2}} + \frac{{h_{21}^{*}n_{2,1}} + {h_{22}n_{2,2}^{*}}}{\sigma_{2}^{2}}}}\end{matrix} & (34) \\\begin{matrix}{{\hat{s}}_{2} = {{\left( {\frac{{h_{11}}^{2} + {h_{12}}^{2}}{\sigma_{1}^{2}} + \frac{{h_{21}}^{2} + {h_{22}}^{2}}{\sigma_{2}^{2}}} \right)s_{2}} + .}} \\{\mspace{50mu}{\frac{{h_{12}^{*}n_{1,1}} - {h_{11}n_{1,2}^{*}}}{\sigma_{1}^{2}} + \frac{{h_{22}^{*}n_{2,1}} - {h_{21}n_{2,2}^{*}}}{\sigma_{2}^{2}}}}\end{matrix} & \;\end{matrix}$

From equation (34), it follows that

$\begin{matrix}{{{SNR}_{IW} = {\left( {\frac{{h_{11}}^{2} + {h_{12}}^{2}}{\sigma_{1}^{2}} + \frac{{h_{21}}^{2} + {h_{22}}^{2}}{\sigma_{2}^{2}}} \right){s_{1}}^{2}}},} & (35)\end{matrix}$which, as expected, degenerates to equation (30) when σ₁ ²=σ₂ ².

The gain of interference-weighted signal combining over conventionalsignal combining, in which equal weighting is used, can be calculatedfrom equations (35) and (29) as an SNR gain

$\begin{matrix}\begin{matrix}{\gamma = {\frac{{SNR}_{IW}}{{SNR}_{EW}} = \frac{\frac{{h_{11}}^{2} + {h_{12}}^{2}}{\sigma_{1}^{2}} + \frac{{h_{21}}^{2} + {h_{22}}^{2}}{\sigma_{2}^{2}}}{\frac{\left( {{h_{11}}^{2} + {h_{12}}^{2} + {h_{21}}^{2} + {h_{22}}^{2}} \right)^{2}}{{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)\sigma_{1}^{2}} + {\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)\sigma_{2}^{2}}}}}} \\{{= \frac{\begin{matrix}{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)^{2} + {\left( {\frac{\sigma_{1}^{2}}{\sigma_{2}^{2}} + \frac{\sigma_{2}^{2}}{\sigma_{1}^{2}}} \right)\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)}} \\{\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right) + \left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)^{2}}\end{matrix}}{\left( {{h_{11}}^{2} + {h_{12}}^{2} + {h_{21}}^{2} + {h_{22}}^{2}} \right)^{2}}},} \\{= {K_{1} + {K_{2}\left( {\frac{\sigma_{1}^{2}}{\sigma_{2}^{2}} + \frac{\sigma_{2}^{2}}{\sigma_{1}^{2}}} \right)} + K_{3}}}\end{matrix} & (36)\end{matrix}$where

$\begin{matrix}{K_{1} = \frac{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)^{2}}{\left( {{h_{11}}^{2} + {h_{12}}^{2} + {h_{\; 21}}^{2} + {h_{22}}^{2}} \right)^{2}}} & (37) \\{K_{2} = \sqrt{K_{1}K_{3}}} & \; \\{K_{3} = {\frac{\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)^{2}}{\left( {{h_{11}}^{2} + {h_{12}}^{2} + {h_{\; 21}}^{2} + {h_{22}}^{2}} \right)^{2}}.}} & \;\end{matrix}$

From equations (36) and (37), it can be seen that K₁ and K₃ aredetermined by the channel matrix, and the only term in equation (13)that is influenced by interference unbalancing is

$\begin{matrix}{{\eta = {K_{2}\left( {\frac{\sigma_{1}^{2}}{\sigma_{2}^{2}} + \frac{\sigma_{2}^{2}}{\sigma_{1}^{2}}} \right)}},} & (38)\end{matrix}$which can be dominant when σ₁ ²≠σ₂ ².

For interference-weighted signal combining, σ₁ ² and σ₂ ² are to beestimated. From equation (32), we haveP _(noise)(ŝ _(1,1))=E[|ŝ _(1,1)−(|h ₁₁|² +|h ₁₂|²)s ₁|²]=(|h ₁₁|² +|h₁₂|²)σ₁ ²P _(noise)(ŝ _(1,2))=E[|ŝ _(1,2)−(|h ₂₁|² +|h ₂₂|²)s ₁|²]=(|h ₂₁|² +|h₂₂|²)σ₂ ²P _(noise)(ŝ _(2,1))=E[|ŝ _(2,1)−(|h ₁₁|² +|h ₁₂|²)s ₂|²]=(|h ₁₁|² +|h₁₂|²)σ₁ ²P _(noise)(ŝ _(2,2))=E[|ŝ _(2,2)−(|h ₂₁|² +|h ₂₂|²)s ₂|²]=(|h ₂₁|² +|h₂₂|²)σ₂ ².  (39)

Therefore, from equations (31)-(33) and (39),

$\begin{matrix}{{\sigma_{1}^{2} \approx {\sum\limits_{\;}^{\;}\;\left( {{{\frac{{\hat{s}}_{1,1}}{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)} - s_{1}}}^{2} + {{\frac{{\hat{s}}_{2,1}}{\left( {{h_{11}}^{2} + {h_{12}}^{2}} \right)} - s_{2}}}^{2}} \right)}}{\sigma_{2}^{2} \approx {\sum\limits_{\;}^{\;}\;{\left( {{{\frac{{\hat{s}}_{1,2}}{\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)} - s_{1}}}^{2} + {{\frac{{\hat{s}}_{2,2}}{\left( {{h_{21}}^{2} + {h_{22}}^{2}} \right)} - s_{2}}}^{2}} \right).}}}} & (40)\end{matrix}$

The summation in equation (40) is preferably over sub-carriers withinthe coherence bandwidth for OFDM based systems or the coherence time forTime Division Multiplexing (TDM) based systems. As such, estimationresults can be applied to groups of signal components within the samecoherence bandwidth or time.

It will be apparent that s₁ and s₂ are not initially known when acommunication signal is received. To use equation (40), hard-decisionresults derived from equation (27) are used instead. Although eachindividual receiving antenna can be relied upon for hard-decisionresults for interference estimates, equation (27) provides not onlyhigher order diversity, but also statistically higher SNR as well,relative to interference-weighted signal combining. Where hard-decisionresults for s₁ and s₂ are used in equation (40), it will be apparentthat σ₁ ² and σ₂ ² are actually represented by the norm between thenormalized received signals and their hard-decision results.

It should be noted that first-pass hard-decision results are used forinterference estimation, and the interference estimates are then used toweight signals during decoding. The interference estimates or weightsdetermined on the basis of such estimates are applied in the decoder 37of FIG. 3. Therefore, although initial hard-decision results used forinterference estimation may be determined in accordance withconventional techniques and thus prone to error as described above,subsequent weighting of signals during decoding at least partiallycompensates the effects of unbalanced interference.

Interference-weighted STTD signal combining in an STTD decoder asdescribed above does not change the diversity order, but improves theSNR of the combined signals.

In embodiments of the invention such as shown in FIG. 3, in whichinterference weighting is applied to decoding operations, no furtherweighting for interference is needed for demodulation or demapping. Withinterference-weighted STTD signal combining as described above, forexample, interference is effectively normalized. In fact, equation (34)can be rewritten asŝ ₁=(|{tilde over (h)} ₁₁|² +|{tilde over (h)} ₁₂|² +|{tilde over (h)}₂₁|² +|{tilde over (h)} ₂₂|²)s ₁ +{tilde over (h)} ₁₁ *ñ _(1,1) +{tildeover (h)} ₁₂ ñ _(1,2) *+{tilde over (h)} ₂₁ *ñ _(2,1) +{tilde over (h)}₂₂ ñ _(2,2)*ŝ ₂=(|{tilde over (h)} ₁₁|² +|{tilde over (h)} ₁₂|² +|{tilde over (h)}₂₁|² +|{tilde over (h)} ₂₂|²)s ₂ +{tilde over (h)} ₁₂ *ñ _(1,1) +{tildeover (h)} ₁₁ ñ _(1,2) *+{tilde over (h)} ₂₂ *ñ _(2,1) +{tilde over (h)}₂₁ ñ _(2,2)*,  (41)where

${{\overset{\sim}{h}}_{11} = \frac{h_{11}}{\sigma_{1}}},{{\overset{\sim}{h}}_{12} = \frac{h_{12}}{\sigma_{1}}},{{\overset{\sim}{h}}_{21} = \frac{h_{21}}{\sigma_{2}}},{{\overset{\sim}{h}}_{22} = \frac{h_{22}}{\sigma_{2}}},$and ñ_(1,1), ñ_(1,2), ñ_(2,1), ñ_(2,2) have unit variance.

As described above, interference weighting may be applied to eitherdemodulation or decoding in an STTD system. Similarly, both theseembodiments of interference weighting may also be applied to MIMOsystems. A MIMO system having an ML decoder, and in which theinterference experienced by each receiving antenna is not the same, isdescribed below as a further example of a system to whichinterference-weighted signal combining may be applied.

Let {right arrow over (σ)}=[σ₁ ² σ₂ ² . . . σ_(N) ²]^(T) be the variancevector corresponding to a transmitted signal {right arrow over (s)},having elements (σ_(i) ², 1≦i≦N) that are not necessarily equal to eachother. Then the ML decoder defined in equation (20) is modified to

$\begin{matrix}{{\hat{\overset{\rightharpoonup}{s}} = {\min{\frac{\overset{\rightharpoonup}{r} - {H\;\overset{\rightharpoonup}{s}}}{\overset{\rightharpoonup}{\sigma}}}}},} & (42)\end{matrix}$where the division is element-based. In other words, according to anembodiment of the invention, an ML decoder is interference weighted whenreceive antennas do not experience the same level of interference. So,to perform interference-weighted ML detection, the distribution ofinterference power among receive antennas is either estimated or assumedto be uniform. Generally speaking, it can be safely assumed that theaverage interference power experienced by each receive antenna is thesame for all the receive antennas. However, due to Rayleigh fading, forexample, the short-term interference power experienced by each receiveantenna can be different.

One advantage of ML decoding over such other encoding schemes aszero-forcing (ZF) and Minimum Mean Squared Error (MMSE) decoding is thatnoise is not enhanced by ML decoding. From equation (20), it can be seenthat no noise enhancement occurs during ML decoding. Although ZF andMMSE decoding provide for interference cancellation, as will beappreciated by those skilled in the art, classical ML decoding is notoptimized for interference cancellation, such that each component of areceived signal can individually be weighted by its own interferencepower in accordance with an aspect of the invention, as shown inequation (42).

From equation (20), it follows that the elements σ_(i) ² of {right arrowover (σ)}, for implementation of decoding according to equation (42),are given by

$\begin{matrix}\begin{matrix}{\sigma_{i}^{2} = {E\left\lbrack {{r_{i} - {H_{i}\overset{\_}{s}}}}^{2} \right\rbrack}} \\{\approx {\frac{1}{N_{c}}{\sum\limits_{k = 1}^{N_{c}}{{{{r_{i}\left( {{vN}_{c} + k} \right)} - {\sum\limits_{j = 1}^{M}{{h_{ij}\left( {{vN}_{c} + k} \right)}{s_{j}\left( {{vN}_{c} + k} \right)}}}}}^{2}.}}}}\end{matrix} & (43)\end{matrix}$

However, as described above, the transmitted signal {right arrow over(s)} is unknown at the time of interference estimation, so first-cuthard-decision estimates {right arrow over (ŝ)}, obtained from equation(20), are used in equation (43) to estimate interference. The estimatedelements σ_(i) ² are then used to weight |r_(i)−H_(i){right arrow over(s)}|², with a weighting factor of

$\frac{1}{\sigma_{i}^{2}}.$Next, interference-weighted hard-decision estimates {right arrow over(ŝ)} from equation (42) are obtained.

Where the first-cut and interference-weighted hard decisions disagree,by more than some predetermined threshold amount or tolerance, forexample, then the estimation and weighting steps are preferablyrepeated, using the interference-weighted hard decision from a precedingiteration. Since most of the elements in {right arrow over (ŝ)} willlikely agree with each other, interference power estimates should bemore reliable than one single element in {right arrow over (ŝ)}.Therefore, when the first-cut and interference-weighted hard-decisionresults do not agree with each other, as determined on the basis of aEuclidean distance therebetween, for example, it is more likely that theinterference-weighted hard-decision results obtained using equation (42)are correct. By repeating the estimation and weighting operations, σ_(i)², r_(i) and H_(i) are actually being updated for soft-demapping.Changes in these parameters should be small in most cases, but the finalresults are better refined.

After one or more iterations of interference estimation and weightinghave been completed, soft demapping of the decoded signal is performed

$\begin{matrix}\begin{matrix}{{\Lambda\left( b_{l} \right)} = {{\min\limits_{{\overset{\rightharpoonup}{s} = {f{(\overset{\rightharpoonup}{b})}}},{b_{l} = 0}}{\sum\limits_{i = 1}^{N}{{r_{i} - {\sum\limits_{j = 1}^{M}{h_{ij}s_{j}}}}}^{2}}} -}} \\{\min\limits_{{\overset{\rightharpoonup}{s} = {f{(\overset{\rightharpoonup}{b})}}},{b_{l} = 1}}{\sum\limits_{i = 1}^{N}{{{r_{i} - {\sum\limits_{j = 1}^{M}{h_{ij}s_{j}}}}}^{2}.}}}\end{matrix} & (44)\end{matrix}$

As described above, no further interference weighting is needed duringdemapping where decoding involves interference weighting.

The above examples illustrate interference weighting of receivedcommunication signals during either decoding or demodulation.

FIG. 4 is a flow diagram of a method in accordance with an embodiment ofthe invention. In the method 40, a communication signal is received at42. It will be apparent from the foregoing that the communication signalreceived at 42 comprises a plurality of signal components, and maycomprise multiple communication signals, as in the case of STTD and MIMOfor example. At 44, interference by which the received communicationsignal components are affected is estimated. The interference estimate,or a weight determined on the basis of the estimate, is then used at 46to weight the signal components of the communication signal at 46, andthe weighted signal is processed at 48. Examples of interferenceestimation, weighting, and processing at 44, 46, and 48 have beendescribed in detail above. In the examples described above withreference to FIG. 2, interference weighting is applied to signalcomponents during demodulation or demapping, whereas in the examplesrelating to FIG. 3, interference weighting is performed during decodingoperations.

Although shown as separate operations in FIG. 4, it will be apparentthat there may be some overlap between these operations in someembodiments of the invention. For example, as described above,interference estimation at 44 may use first-pass hard-decision valuesgenerated during decoding of a received signal. The interferenceestimate, or a weight determined using the estimate, is then applied tothe received signal, such as during decoding or demodulation of thesignal.

The overall effect of interference weighting varies according to thedegree of interference imbalance. FIG. 5 is a plot illustrating anexample SNR gain characteristic relative to interference imbalance.

FIG. 5 shows SNR gain γ, defined above in equation (36), versusinterference ratio

$\frac{\sigma_{1}^{2}}{\sigma_{2}^{2}}$for interference-weighted 2-by-2 STTD signal combining as describedabove. Solely for illustrative purposes, it has been assumed thatK₁=K₃=0.5 which, from equation (37), gives K₂=0.5. From equation (36),when σ₁ ²=σ₂ ², γ=1. It is apparent from FIG. 5 that the speed ofperformance deterioration for non-weighted signal combining relative tointerference-weighted combining increases when interference unbalancingbetween the two receiving antennas becomes more severe.

What has been described is merely illustrative of the application of theprinciples of the invention. Other arrangements and methods can beimplemented by those skilled in the art without departing from thespirit and scope of the present invention.

For example, the invention is in no way restricted to the above examplesof STTD and MIMO. Embodiments of the invention may be implemented invirtually any system affected by coloured or bursty interference.Interference weighting according to aspects of the invention is local inthat no coordination among neighbouring base stations or coverage areasis required), simple in that interference estimation and weighting arenot expensive in terms of DSP real-time, and effective in that theperformance of a Turbo code, for instance, in a coloured-interferenceenvironment can be improved.

Interference weighting is also not restricted to particular modulations.Although QAM is described in detail above, the interference weightingmay also be used in conjunction with other modulation schemes, includingphase shift keying modulation techniques such as quadrature phase shiftkeying (QPSK) for instance, which is also common in STTD systems.

The invention is similarly applicable to other types of decoders. In theabove description, for example, MIMO is presented in the context of MLdecoding, although ZF/MMSE decoding may also be implemented in a MIMOsystem. One possible issue to consider when selecting ML or ZF/MMSEdecoding is whether ZF/MMSE would provide better performance when thepower distribution of {right arrow over (n)} in equation (18) isunknown. Applying the Moore-Penrose pseudo-inverse matrix G=(H*H)⁻¹H* toequation (18),{right arrow over (s)}=(H*H)⁻¹ H*{right arrow over (r)}−(H*H)⁻¹ H*{rightarrow over (n)}.  (45)It is clear that the power distribution of {right arrow over (n)} is nottaken into account in the calculation of the Moore-Penrosepseudo-inverse matrix G. Note also that equation (45) is designed forinterference cancellation, and no consideration is typically given tooptimal signal-combining to achieve better SNR.

However, interference weighting according to aspects of the inventionmay still be applied in conjunction with a receiver in which ZF/MMSEdecoding is implemented. If {right arrow over (g)}_(i) denotes the i-throw of the Moore-Penrose pseudo-inverse matrix G=(H*H)⁻¹H*, then fromequation (45), the noise power in {tilde over (s)}_(i) is given by

$\begin{matrix}{{{\sigma^{2}\left( {\overset{\sim}{s}}_{i} \right)} = {\sum\limits_{j}^{\;}{{g_{ij}}^{2}\sigma_{j}^{2}}}},} & (46)\end{matrix}$where g_(ij) is an element in G, and{right arrow over ({tilde over (s)}=G{right arrow over (r)}={right arrowover (s)}+G{right arrow over (n)}.  (47)

Since a ZF system is optimized for interference cancellation, anynormalization of the rows in H will be compensated in the calculation ofG according to the known SIR criterion. So for ZF, it can be concludedthat no interference weighting can be applied at the decoding stage.This does not preclude weighting at a demodulation or demapping stage.At a soft-demapping stage, the demapped value of {tilde over (s)}_(i) ispreferably weighted by the factor of

$\frac{1}{\sigma^{2}\left( {\overset{\sim}{s}}_{i} \right)},$where σ²({tilde over (s)}_(i)) is calculated according to equation (42).Note that when σ_(j) ² remains a constant across antennas andsub-carriers, equation (46) is degenerated to

$\begin{matrix}{{\sigma^{2}\left( {\overset{\sim}{s}}_{i} \right)} = {\sum\limits_{j}^{\;}{{g_{ij}}^{2}.}}} & (48)\end{matrix}$

A determination as to whether receiving antennas will experiencedifferent levels of interference is often known at the time of systemdeployment, and a conventional or interference-weighted decoding ordemodulation algorithm may thus be selected accordingly. However, asinterference weighting in accordance with aspects of the inventionestimates interference and performs weighting accordingly,interference-weighted received signal processing effectivelyautomatically adjusts itself for a current operating environment andthus may be used whether or not coloured interference is expected.

1. A communication signal receiver comprising: an input for receiving acommunication signal, the communication signal comprising signalcomponents; and a processor adapted to: receive the communication signalfrom the input, to estimate interference in the communication signal,wherein a first interference estimate is based on a first modulatedsymbol of a first signal component in the communication signal and asecond interference estimate is based on a second modulated symbol of asecond signal component in the communication signal; apply a firstweight based on the first interference estimate to a first signalcomponent of the communication signal; and apply a second weight basedon the second interference estimate to a second signal component of thecommunication signal.
 2. The communication signal receiver of claim 1,wherein the first signal component is included in a first group ofsignal components of the received communication signal, wherein thesecond signal component is included in a second group of signalcomponents of the received communication signal, and wherein theprocessor is further adapted to apply the first weight to each signalcomponent of the first group of signal components and to apply thesecond weight to each signal component of the second group of signalcomponents.
 3. The communication signal receiver of claim 2, wherein thecommunication signal comprises Orthogonal Frequency DivisionMultiplexing (OFDM) signals, and wherein the signal components comprisesub-carrier signals.
 4. The communication signal receiver of claim 3,wherein each of the first and second groups of signal componentsincludes sub-carrier signals within a coherence bandwidth of the OFDMsignals.
 5. The communication signal receiver of claim 1, wherein theinput is adapted for connection to an antenna system.
 6. Thecommunication signal receiver of claim 5, wherein the antenna systemcomprises a single antenna.
 7. The communication signal receiver ofclaim 6, wherein the single antenna is configured to receivecommunication signals from a plurality of transmitting antennas.
 8. Thecommunication signal receiver of claim 5, wherein the antenna systemcomprises a plurality of antennas.
 9. The communication signal receiverof claim 8, wherein each of the plurality of antennas is configured toreceive communication signals from a plurality of transmitting antennas.10. The communication signal receiver of claim 1, wherein the processorcomprises an interference estimator for estimating interference in thecommunication signal and a decoder adapted for connection to theinterference estimator and to the input, and wherein the input isadapted for connection to an antenna system.
 11. The communicationsignal receiver of claim 10, wherein the processor further comprises ademodulator adapted for connection to the decoder for demodulatingdecoded communication signal components output from the decoder, and amultiplier connected to the demodulator and to the interferenceestimator for applying the first and second weights to first and seconddemodulated communication signal components output from the demodulator.12. The communication signal receiver of claim 11, wherein thedemodulator is selected from the group consisting of a QuadratureAmplitude Modulation (QAM) demodulator and a Quadrature Phase ShiftKeying (QPSK) demodulator.
 13. The communication signal receiver ofclaim 10, wherein the decoder combines weighted signal components ofdifferent received communication signals to generate decodedcommunication signals.
 14. The communication signal receiver of claim13, wherein the decoder is selected from the group consisting of aSpace-Time Transmit Diversity (STTD) decoder and a Multiple InputMultiple Output (MIMO) decoder.
 15. A method of processing communicationsignals comprising: receiving a communication signal, the communicationsignal comprising a plurality of signal components; estimatinginterference affecting the received communication signal to obtain atleast a first interference estimate based on a first modulated symbol ofa first signal component from the plurality of signal components and asecond interference estimate based on a second modulated symbol of asecond signal component from the plurality of signal components; andapplying a first weight based on the first interference estimate to afirst signal component of the plurality of signal components, andapplying a second weight based on the second interference estimate to asecond signal component of the plurality of signal components.
 16. Themethod of claim 15, further comprising decoding the weighted signalcomponents.
 17. The method of claim 16, wherein decoding comprisescombining weighted signal components of different received communicationsignals.
 18. The method of claim 17, wherein receiving comprisesreceiving a plurality of communication signals.
 19. The method of claim18, wherein the communication signals comprise diversity signals in aSpace-Time Transmit Diversity (STTD) system.
 20. The method of claim 19,wherein the diversity signals are Orthogonal Frequency DivisionMultiplexing (OFDM) signals, and wherein the plurality of signalcomponents comprises sub-carrier signals.
 21. The method of claim 20,wherein the STTD system is a 2-by-2 STTD system, and wherein combiningcomprises combining according to${{\hat{s}}_{1}(k)} = {{\left( {\frac{{{h_{11}(k)}}^{2} + {{h_{12}(k)}}^{2}}{\sigma_{1}^{2}(k)} + \frac{{{h_{21}(k)}}^{2} + {{h_{22}(k)}}^{2}}{\sigma_{2}^{2}(k)}} \right){s_{1}(k)}} + \frac{{{h_{11}^{*}(k)}{n_{1,1}(k)}} + {{h_{12}(k)}{n_{1,2}^{*}(k)}}}{\sigma_{1}^{2}(k)} + {\frac{{{h_{21}^{*}(k)}{n_{2,1}(k)}} + {{h_{22}(k)}{n_{2,2}^{*}(k)}}}{\sigma_{2}^{2}(k)}\mspace{14mu}{and}}}$${{\hat{s}}_{2}(k)} = {{\left( {\frac{{{h_{11}(k)}}^{2} + {{h_{12}(k)}}^{2}}{\sigma_{1}^{2}(k)} + \frac{{{h_{21}(k)}}^{2} + {{h_{22}(k)}}^{2}}{\sigma_{2}^{2}(k)}} \right){s_{2}(k)}} + \frac{{{h_{12}^{*}(k)}{n_{1,1}(k)}} - {{h_{11}(k)}{n_{1,2}^{*}(k)}}}{\sigma_{1}^{2}(k)} + \frac{{{h_{22}^{*}(k)}{n_{2,1}(k)}} - {{h_{21}(k)}{n_{2,2}^{*}(k)}}}{\sigma_{2}^{2}(k)}}$where k is a sub-carrier index; h₁₁, h₁₂, h₂₁, and h₂₂ are elements in a2-by-2 STTD channel matrix; σ₁ ² and σ₂ ² are interference estimates fors₁ and s₂; s₁ and s₂ are transmitted communication signals; and n_(1,1),n_(1,2), n_(2,1), and n_(2,2) are noise signals.
 22. The method of claim21, wherein the signal combining uses hard-decision estimates of s₁ (k)and s₂ (k).
 23. The method of claim 21, wherein estimating comprisesestimating σ₁ ² and σ₂ ² as $\sigma_{1}^{2} \approx {\sum\begin{pmatrix}{{{\frac{{\hat{s}}_{1,1}(k)}{\left( {{{h_{11}(k)}}^{2} + {{h_{12}(k)}}^{2}} \right)} - {s_{1}(k)}}}^{2} +} \\{{\frac{{\hat{s}}_{2,1}(k)}{\left( {{{h_{11}(k)}}^{2} + {{h_{12}(k)}}^{2}} \right)} - {s_{2}(k)}}}^{2}\end{pmatrix}}$ ${\sigma_{2}^{2} \approx {\sum\begin{pmatrix}{{{\frac{{\hat{s}}_{1,2}(k)}{\left( {{{h_{21}(k)}}^{2} + {{h_{22}(k)}}^{2}} \right)} - {s_{1}(k)}}}^{2} +} \\{{\frac{{\hat{s}}_{2,2}(k)}{\left( {{{h_{21}(k)}}^{2} + {{h_{22}(k)}}^{2}} \right)} - {s_{2}(k)}}}^{2}\end{pmatrix}}},$ where s_(i,j) (i=1, 2; j=1, 2) represents acommunication signal received at antenna j corresponding to atransmitted communication signal s_(i); and the summations are over acoherence bandwidth of the OFDM signals.
 24. The method of claim 23,wherein the estimating uses hard-decision estimates of s₁(k) and s₂(k).25. The method of claim 15, wherein receiving comprises receiving aplurality of communication signals, and wherein the communicationsignals comprise received communication signals in a Multiple InputMultiple Output (MIMO) system.
 26. The method of claim 25, whereindecoding comprises interference-weighted hard-decision decodingaccording to${\hat{\overset{\rightharpoonup}{s}} = {\min{\frac{\overset{\rightharpoonup}{r} - {H\;\overset{\rightharpoonup}{s}}}{\overset{\rightharpoonup}{\sigma}}}}},$where {right arrow over (r)} is a vector comprising the receivedcommunication signals; H is a channel matrix of the MIMO system; {rightarrow over (s)} is a vector comprising transmitted communicationsignals; and {right arrow over (σ)} is a vector comprising interferenceestimates.
 27. The method of claim 26, wherein applying comprisesapplying at least a first weight to each signal component in a firstgroup of signal components and a second weight to each signal componentin a second group of signal components, and wherein the interferenceestimates σ_(i) ² of {right arrow over (σ)} are estimated as$\begin{matrix}{{{{\sigma_{i}^{2} = {{E\left\lbrack {{r_{i} - {H_{i}s}}}^{2} \right\rbrack} \approx {\frac{1}{N_{c}}\sum\limits_{k = 1}^{N_{c}}}}}}{r_{i}\left( {{vN}_{c} + k} \right)}} -} \\{{{\sum\limits_{j = 1}^{M}{{h_{ij}\left( {{vN}_{c} + k} \right)}{s_{j}\left( {{vN}_{c} + k} \right)}}}}^{2},}\end{matrix}$ where r_(i) is the i^(th) element of {right arrow over(r)}; (νN_(c)+k) is a signal component index; H_(i) is the i^(th) row ofH; ν is an integer; N_(c) is a number of signal components in each ofthe first and second groups of signal components; M is a number oftransmitted communication signals in the MIMO system; h_(ij) is anelement of H; and s_(j) is the j^(th) element of {right arrow over (s)}.28. The method of claim 26, wherein decoding further comprisesgenerating first-cut hard-decision estimates of the transmittedcommunication signals in {right arrow over (s)} according to {rightarrow over (ŝ)}=min∥{right arrow over (r)}−H{right arrow over (s)}∥ andusing the first-cut hard-decision estimates for {right arrow over (s)}in the interference-weighted hard-decision decoding.
 29. The method ofclaim 28, further comprising determining whether the first-cuthard-decision estimates are within a tolerance of theinterference-weighted hard-decision decoding results, and if not,repeating the interference-weighted hard-decision decoding using theinterference-weighted hard-decision results for {right arrow over (s)}.30. The method of claim 29, wherein determining whether the first-cuthard-decision estimates are within a tolerance of theinterference-weighted hard-decision decoding results comprises measuringthe Euclidean distance between the first-cut hard-decision estimates andthe interference-weighted hard-decision decoding results.
 31. The methodof claim 15, further comprising demodulating the plurality of signalcomponents to generate demodulated signal components, wherein applying arespective weight comprises applying respective weights to at least twoof the demodulated signal components.
 32. The method of claim 31,wherein receiving comprises receiving a plurality of communicationsignals, wherein demodulating comprises determining a log likelihoodratio (LLR) {tilde over (Λ)}(b₁) of each bit b₁ of a modulation symbol{right arrow over (b)} mapped to signal components of transmittedsignals respectively corresponding to the plurality of signal componentsof the received communication signals, and wherein applying respectiveweights comprises applying respective weights to {tilde over (Λ)}(b₁)with respective estimates of interference power α.
 33. The method ofclaim 32, further comprising decoding the received communication signalsto generate decoded communication signal components, and whereindemodulating comprises demodulating the decoded signal components. 34.The method of claim 33, wherein the decoded signal components comprise aplurality of decoder output signal components {tilde over (s)}₁(k) and aplurality of decoder output signal components {tilde over (s)}₂(k), andwherein demodulating comprises determining the LLR for each decoderoutput signal component as $\begin{matrix}{{\overset{\sim}{\Lambda}\left( b_{l} \right)} = {{\min\limits_{{s_{i} = {f{(b)}}},{b_{l} = 0}}{{\frac{{\overset{\sim}{s}}_{i}(k)}{\left( {{{h_{1}(k)}}^{2} + {{h_{2}(k)}}^{2}} \right)} - {s_{i}(k)}}}^{2}} -}} \\{\min\limits_{{s_{i} = {f{(b)}}},{b_{l} = 1}}{{\frac{{\overset{\sim}{s}}_{i}(k)}{\left( {{{h_{1}(k)}}^{2} + {{h_{2}(k)}}^{2}} \right)} - {s_{i}(k)}}}^{2}}\end{matrix}$ and weighting {tilde over (Λ)}(b₁) with${\alpha = \frac{\left( {{{h_{1}(k)}}^{2} + {{h_{2}(k)}}^{2}} \right)^{2}}{{{{h_{1}(k)}}^{2}\sigma_{1}^{2}} + {{{h_{2}(k)}}^{2}\sigma_{2}^{2}}}},$where k is a signal component index; i=1, 2; h₁ and h₂ are elements in achannel matrix; σ₁ ² and σ₂ ² are interference estimates for s₁ and s₂;and s₁ and s₂ are transmitted communication signals.
 35. The method ofclaim 34, further comprising estimating α according to$\frac{1}{\alpha} \approx {\sum\limits_{i = 1}^{N_{c}}{{{\frac{\overset{\sim}{s_{1}}\left( {{vN}_{c} + i} \right)}{{{h_{1}\left( {{vN}_{c} + i} \right)}}^{2} + {{h_{2}\left( {{vN}_{c} + i} \right)}}^{2}} - {{\hat{s}}_{1}\left( {{vN}_{c} + i} \right)}}}^{2}\mspace{14mu}{and}}}$${\frac{1}{\alpha} \approx {\sum\limits_{i = 1}^{N_{c}}{{\frac{\overset{\sim}{s_{2}}\left( {{vN}_{c} + i} \right)}{{{h_{1}\left( {{vN}_{c} + i} \right)}}^{2} + {{h_{2}\left( {{vN}_{c} + i} \right)}}^{2}} - {{\hat{s}}_{2}\left( {{vN}_{c} + i} \right)}}}^{2}}},$where ν is an integer; N_(c) is a number of signal components in each ofthe plurality of groups of signal components; (νN_(c)+i) and (k) denoteparticular ones of the plurality of signal components; and ŝ₁(νN_(c)+i)and ŝ₂(νN_(c)+i) are hard-decision estimates of s₁(νN_(c)+i) andŝ₂(νN_(c)+i).
 36. The method of claim 35, wherein the receivedcommunication signals comprise signals r_(i), i=1, . . . , N, whereinthe decoded communication signal components comprise decoder outputsignals s_(i), i=1, . . . , M, each comprising a plurality of decodedsignal components, and wherein demodulating comprises determining theLLR as $\begin{matrix}{{\overset{\sim}{\Lambda}\left( b_{l} \right)} = {{\min\limits_{{\overset{\_}{s} = {f{(\overset{\_}{b})}}},{b_{l} = 0}}{\sum\limits_{i = 1}^{N}{{{r(k)}_{i} - {\sum\limits_{j = 1}^{M}{{h_{ij}(k)}{s_{j}(k)}}}}}^{2}}} -}} \\{\min\limits_{{\overset{\_}{s} = {f{(\overset{\_}{b})}}},{b_{l} = 1}}{\sum\limits_{i = 1}^{N}{{{r_{i}(k)} - {\sum\limits_{j = 1}^{M}{{h_{ij}(k)}{s_{j}(k)}}}}}^{2}}}\end{matrix}$ and weighting {tilde over (Λ)}(b₁) with${\alpha = \frac{1}{\sigma^{2}}},$ where k is a signal component index;σ² is an interference estimate; and h_(ij) is an element of a channelmatrix.
 37. The method of claim 36, further comprising estimating αaccording to${\frac{1}{\alpha} = {\sigma^{2} \approx {\sum\limits_{k = 1}^{N_{c}}{\sum\limits_{i = 1}^{N}{{{r_{i}\left( {{vN}_{c} + k} \right)} - {{H_{i}\left( {{vN}_{c} + k} \right)}{\overset{\rightarrow}{s}\left( {{vN}_{c} + k} \right)}}}}^{2}}}}},$where H_(i) is the i^(th) row of H; ν is an integer; N_(c) is a numberof signal components in each of the plurality of groups of signalcomponents; and {right arrow over (s)} is a vector comprisingtransmitted communication signals.
 38. A computer program productcomprising a non-transitory computer-readable medium storinginstructions which, when executed by a processor, perform the method ofclaim
 15. 39. A communication signal receiver system for OrthogonalFrequency Division Multiplexing (OFDM) communications, comprising: anantenna system for receiving an OFDM communication signal having aplurality of sub-carrier signal components; means for estimatinginterference in the received communication signal to obtain at least afirst interference estimate and a second interference estimate whereineach interference estimate is based on at least one modulated symbol ofone of the plurality of sub-carrier components; means for applying afirst weight based on the first interference estimate to a firstsub-carrier signal component of the plurality of sub-carrier signalcomponents, and to apply a second weight based on the secondinterference estimate to a second sub-carrier signal component of theplurality of sub-carrier signal components; and means for processing theweighted signal components.
 40. The communication signal receiver systemof claim 39, wherein the antenna system comprises at least one antennafor receiving a communication signal from at least one transmittingantenna.
 41. The communication signal receiver system of claim 40,wherein the means for processing comprises means for decoding and meansfor demodulating.
 42. The communication signal receiver system of claim41, wherein the means for decoding combines the weighted sub-carriersignal components from a plurality of received OFDM communicationsignals.
 43. The communication signal receiver system of claim 39,wherein each of the first and second sub-carrier signal components isincluded in a respective group of sub-carrier signal components within acoherence bandwidth of an OFDM communication signal, wherein each of thefirst and second weights is a group weight, and wherein the means forapplying applies the group weight to each sub-carrier signal componentin the group.
 44. A communication signal receiver comprising: an inputfor receiving a communication signal, the communication signalcomprising signal components; and a processor adapted to: receive thecommunication signal from the input, wherein the communication signalcomprises a plurality of sub-carrier signal components; generate a firstinterference estimate for a first signal component from the plurality ofsub-carrier signal components of the communication signal, wherein thefirst interference estimate is based on at least one modulated symbol ofone of the plurality of sub-carrier components; and apply a first weightbased on the first interference estimate to the first signal componentof the communication signal.
 45. The communication signal receiver ofclaim 44, wherein the processor is further adapted to generate a secondinterference estimate for a second signal component from the pluralityof sub-carrier signal components of the communication signal, whereinthe second interference estimate is based on at least one modulatedsymbol of one of the plurality of sub-carrier components, and apply asecond weight based on the second interference estimate to the secondsignal component of the communication signal.